ML Platform Engineer

As an ML Platform Engineer at Pipedrive, you will build and maintain key components of our machine learning platform. This includes managing cloud infrastructure, developing parts of the ML workflow, and ensuring that Data Scientists and ML Engineers have seamless access to the tools they need. You'll also be responsible for monitoring the health and performance of deployed models while troubleshooting any issues that arise.

Candidates for the ML Platform Engineer role at Pipedrive should have at least 3 years of experience in software development, focusing on AWS cloud computing and distributed systems for large-scale data processing. Familiarity with MLOps tooling, infrastructure as code, and proficiency in programming languages like Python, Go, or Scala are also essential.

In a typical day, an ML Platform Engineer at Pipedrive will work on building and managing cloud infrastructures for ML application components, develop key parts of the ML workflow, monitor model performance, and provide technical support across departments. You'll collaborate closely with Data Scientists and ML Engineers, ensuring they can leverage the resources effectively.

At Pipedrive, we prioritize personal and professional development for our team members. We offer internal and external training opportunities, mentoring, and the chance to attend industry conferences to enhance skills relevant to the role of ML Platform Engineer. We value continuous learning and provide a work environment that fosters growth.

Pipedrive embraces flexibility, offering hybrid work options for our ML Platform Engineer role. You can work from our beautifully designed Prague office or choose to work remotely, ensuring you have the balance that suits your lifestyle while still being an integral part of our team.

The Feature Store platform is a critical component for Data Scientists and ML Engineers at Pipedrive, as it allows seamless access to features used in machine learning models. As an ML Platform Engineer, building and managing this platform means ensuring that the data used is organized, easily retrievable, and optimized for performance, which significantly impacts model accuracy and efficiency.

At Pipedrive, the path for an ML Platform Engineer offers various progression opportunities—starting with deeper technical roles, transitioning to leadership positions, or exploring adjacent fields, such as data science or product management. Our supportive environment encourages you to chart your own course while contributing to impactful projects.

To effectively answer this question, highlight specific projects where you've utilized AWS for building and managing machine learning infrastructures. Mention the services you used, such as S3 for storage, EC2 for compute, or Lambda for serverless applications. Providing examples showcases your hands-on experience and understanding.

Demonstrate your knowledge of security best practices and compliance regulations that are crucial for machine learning platforms. Discuss specific measures such as encryption, role-based access control, and regular audits to maintain compliance while ensuring data integrity.

Share specific examples of challenges you've encountered, such as scaling issues, data consistency, or deployment hurdles. Detail the proactive steps you took to resolve these challenges and what you learned from the experience.

Discuss the MLOps tools you’ve used—like MLflow, Kubeflow, or TFX—and explain your reasons for their selection. Focus on how these tools facilitated smoother workflows, improved collaboration, and enhanced model deployment and monitoring.

Emphasize the importance of comprehensive documentation in your role as an ML Platform Engineer. Explain your methods for documenting components and processes systematically to ensure clarity and accessibility for teams across the organization.

Identify your proficient programming languages and provide examples of projects or tasks where you effectively applied these skills. Describe how your programming experience contributes to your effectiveness as an ML Platform Engineer.

Discuss your strategies for monitoring model performance post-deployment, including metrics you track and tools used for automated monitoring. Detail how you ensure model reliability and what steps you take to address performance degradation.

Share your experience with being on-call and how you prioritize issues in production. Discuss your problem-solving approach and how effective communication with the team and stakeholders plays a role in your incident response plan.

Familiarize yourself with Scrum or Kanban methodologies and illustrate your experience working within these frameworks. Describe how agile principles enhance collaboration, adaptability, and productivity in building and deploying ML systems.

Present your understanding of Pipedrive’s mission and culture, and connect it to your career aspirations. Be sincere about how this role aligns with your values and the unique contributions you hope to make to the team at Pipedrive.